Modeling the causal regulatory network by integrating chromatin accessibility and transcriptome data

被引:0
|
作者
Yong Wang [1 ,2 ]
Rui Jiang [1 ,3 ]
Wing Hung Wong [1 ]
机构
[1] Department of Statistics,Department of Biomedical Data Science,Bio-X Program,Stanford University
[2] Academy of Mathematics and Systems Science,National Center for Mathematics and Interdisciplinary Sciences,Chinese Academy of Sciences
[3] MOE Key Laboratory of Bioinformatics,Bioinformatics Division and Center for Synthetic and Systems Biology,TNLIST,Department of Automation,Tsinghua University
关键词
gene regulatory network; open chromatin; DNA accessibility; transcription factor colocalization; statistical model; data integration;
D O I
暂无
中图分类号
Q78 [基因工程(遗传工程)];
学科分类号
071007 ; 0836 ; 090102 ;
摘要
Cell packs a lot of genetic and regulatory information through a structure known as chromatin,i.e.DNA is wrapped around histone proteins and is tightly packed in a remarkable way.To express a gene in a specific coding region,the chromatin would open up and DNA loop may be formed by interacting enhancers and promoters.Furthermore,the mediator and cohesion complexes,sequence-specific transcription factors,and RNA polymerase Ⅱ are recruited and work together to elaborately regulate the expression level.It is in pressing need to understand how the information,about when,where,and to what degree genes should be expressed,is embedded into chromatin structure and gene regulatory elements.Thanks to large consortia such as Encyclopedia of DNA Elements(ENCODE) and Roadmap Epigenomic projects,extensive data on chromatin accessibility and transcript abundance are available across many tissues and cell types.This rich data offer an exciting opportunity to model the causal regulatory relationship.Here,we will review the current experimental approaches,foundational data,computational problems,interpretive frameworks,and integrative models that will enable the accurate interpretation of regulatory landscape.Particularly,we will discuss the efforts to organize,analyze,model,and integrate the DNA accessibility data,transcriptional data,and functional genomic regions together.We believe that these efforts will eventually help us understand the information flow within the cell and will influence research directions across many fields.
引用
收藏
页码:240 / 251
页数:12
相关论文
共 50 条
  • [41] Identifying causal genes for migraine by integrating the proteome and transcriptome
    Li, Shuang-jie
    Shi, Jing-jing
    Mao, Cheng-yuan
    Zhang, Chan
    Xu, Ya-fang
    Fan, Yu
    Hu, Zheng-wei
    Yu, Wen-kai
    Hao, Xiao-yan
    Li, Meng-jie
    Li, Jia-di
    Ma, Dong-rui
    Guo, Meng-nan
    Zuo, Chun-yan
    Liang, Yuan-yuan
    Xu, Yu-ming
    Wu, Jun
    Sun, Shi-lei
    Wang, Yong-gang
    Shi, Chang-he
    JOURNAL OF HEADACHE AND PAIN, 2023, 24 (01):
  • [42] Identifying causal genes for migraine by integrating the proteome and transcriptome
    Shuang-jie Li
    Jing-jing Shi
    Cheng-yuan Mao
    Chan Zhang
    Ya-fang Xu
    Yu Fan
    Zheng-wei Hu
    Wen-kai Yu
    Xiao-yan Hao
    Meng-jie Li
    Jia-di Li
    Dong-rui Ma
    Meng-nan Guo
    Chun-yan Zuo
    Yuan-yuan Liang
    Yu-ming Xu
    Jun Wu
    Shi-lei Sun
    Yong-gang Wang
    Chang-he Shi
    The Journal of Headache and Pain, 24
  • [43] Theoretical Modeling of Protein Accessibility to the Chromatin Fiber
    Koslover, Elena F.
    de la Rosa, Mario Diaz
    Mulligan, Peter J.
    Spakowitz, Andrew J.
    BIOPHYSICAL JOURNAL, 2010, 98 (03) : 476A - 476A
  • [44] Integrating regulatory DNA sequence and gene expression to predict genome-wide chromatin accessibility across cellular contexts
    Nair, Surag
    Kim, Daniel S.
    Perricone, Jacob
    Kundaje, Anshul
    BIOINFORMATICS, 2019, 35 (14) : I108 - I116
  • [45] Associating divergent lncRNAs with target genes by integrating genome sequence, gene expression and chromatin accessibility data
    Wang, Yongcui
    Chen, Shilong
    Li, Wenran
    Jiang, Rui
    Wang, Yong
    NAR GENOMICS AND BIOINFORMATICS, 2020, 2 (02)
  • [46] Transcriptome data are insufficient to control false discoveries in regulatory network inference
    Kernfeld, Eric
    Keener, Rebecca
    Cahan, Patrick
    Battle, Alexis
    CELL SYSTEMS, 2024, 15 (08)
  • [47] High-throughput sequencing of the transcriptome and chromatin accessibility in the same cell
    Song Chen
    Blue B. Lake
    Kun Zhang
    Nature Biotechnology, 2019, 37 : 1452 - 1457
  • [48] High-throughput sequencing of the transcriptome and chromatin accessibility in the same cell
    Chen, Song
    Lake, Blue B.
    Zhang, Kun
    NATURE BIOTECHNOLOGY, 2019, 37 (12) : 1452 - +
  • [49] Parallel bimodal single-cell sequencing of transcriptome and chromatin accessibility
    Xing, Qiao Rui
    El Farran, Chadi A.
    Zeng, Ying Ying
    Yi, Yao
    Warrier, Tushar
    Gautam, Pradeep
    Collins, James J.
    Xu, Jian
    Droge, Peter
    Koh, Cheng-Gee
    Li, Hu
    Zhang, Li-Feng
    Loh, Yuin-Han
    GENOME RESEARCH, 2020, 30 (07) : 1027 - 1039
  • [50] Single-cell chromatin accessibility and transcriptome atlas of mouse embryos
    Jiang, Shan
    Huang, Zheng
    Li, Yun
    Yu, Chengwei
    Yu, Hao
    Ke, Yuwen
    Jiang, Lan
    Liu, Jiang
    CELL REPORTS, 2023, 42 (03):